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Critical Systems Debate: Big Data, Algorithms, Research and the Question of Ethics

The digital world now generates more than 1.7 billion bytes per minute and computational power is exploding, ushering in the age of big data. No matter the debates about the value of personal data and privacy, big data are here to stay and it is crucial that the ethical dilemmas around the uses of data are addressed. In this interdisciplinary debate we will discuss the ethics of research in collaboration with industry and reliance on industry data management practices, the possibilities of transparency of algorithms in data-driven decision-making and the implications of these questions for the future of big data in research and practice.

Jeff Hancock (Presenter): The Facebook Study: A Personal Account of Big Social Data and Ethics

Abstract: Big social data, such as that produced by Facebook and Twitter, have the potential to transform the social sciences and lead to advances in understanding human behavior. At the same time, novel large-scale methods and forms of collaboration between academia and industry raise new ethical questions. I will discuss the Facebook Emotion study and step through several aspects of the study that involve important ethical decision points, and provide some insights on why the study generated such massive attention and criticism. Lastly, I will discuss some of the personal costs, opportunities and lessons associated with this level and kind of controversy.

Jeff Hancock is a Professor in the Departments of Communication and Information Science at Cornell University. Professor Hancock works on questions concerned with psychological and interpersonal processes that take place online. He specializes in using in using computational linguistic analysis to understand how the words we use can reveal psychological and social dynamics, such as deception and credibility, emotional dynamics, intimacy and relationships, and social support.

Rasmus Pagh (Presenter): Big Data: Fairness and TransparencyAbstract: How can we make sure that decisions made based on big data analysis are fair? For example, that they do not indirectly discriminate based on race or gender? And more generally, how do we ensure the transparency that makes people trust that big data is being used in a fair and ethical way? The talk will discuss these questions and some (very) partial attempts at answers.

Rasmus Pagh is a Professor in the Theoretical Computer Science section at the IT University of Copenhagen. Professor Pagh is part of the Copenhagen Algorithms community and his scientific interests are within algorithms and data structures, with an emphasis on big data. He has worked extensively on basic questions in information retrieval and the role of randomness in computing, on problems with applications in databases and knowledge discovery, and on the exploitation of parallelism in modern computer architectures.

Rachel Douglas Jones (Discussant) is an Assistant Professor in the Inter Section at the IT University of Copenhagen. She was trained as an anthropologist and STS scholar at the University of Cambridge, Harvard University and Durham University. Ethics procedures and ethics reviews of research as mode of governance are topics central to her research. She is also interested in the notion of ethics in Big Data research, decision-making practices that rely on big data and the attendant algorithms charged with making it usable.